We will update this tutorial when necessary. Readers can access the latest version in our GitHub repository.
If you have any questions, errors or bug reports, please contact Pietro Pollo (pietro_pollo@hotmail.com) or Shinichi Nakagawa (snakagaw@ualberta.ca).
This online material is a supplement to our paper “Beyond sex
differences in mean: meta-analysis of differences in skewness, kurtosis,
and correlation”. You will see how to calculate the new effect size
statistics we have proposed and how to use them in a meta-analytical
model using the metafor package in R.
In this online material, we provide details on the simulations we ran to evaluate the effectiveness of our proposed effect size statistics (and their associated sampling error). We also show how to (1) calculate our newly proposed effect sizes (\(\Delta sk\), \(\Delta ku\), \(\Delta Zr\)) and (2) exemplify their use with data from the International Mouse Phenotyping Consortium.
To understand the effectiveness of our proposed effect size statistics at capturing differences in skewness, kurtosis, and correlation between two groups we ran a series of simulations. We simulated two distinct groups that varied in distributional moments (i.e., \(\Delta sk\) and \(\Delta ku\)) or in the correlation within each group (i.e., \(\Delta Zr\)). We used the Pearson function from the moments (Komsta and Novomestky 2022) package to control aspects of skewness and kurtosis in each group. For simulating differences in correlations between groups we used the mvnorm function in the MASS package [].
Our tutorial uses R statistical software and existing
R packages, which you will first need to download and
install.
If the packages are archived in CRAN, use
install.packages() to install them. For example, to install
the metafor , you can execute
install.packages("metafor") in the console (bottom left
pane of R Studio).
Version information of each package is listed at the end of this tutorial.
if (!require("pacman")) {install.packages("pacman")}
pacman::p_load(corrr,
DT,
ggdist,
ggtext,
here,
janitor,
metafor,
pander,
patchwork,
tidyverse)
options(DT.options = list(rownames = FALSE,
dom = "Blfrtip",
scrollX = TRUE,
pageLength = 5,
columnDefs = list(list(targets = '_all',
className = 'dt-center')),
buttons = c('copy', 'csv', 'excel', 'pdf')))
source("layout.R")We also provide some additional helper functions to calculate effect
sizes, process data, and visualise our results. The most straightforward
way to use these custom functions is to run the code chunk below.
Alternatively, paste the code into the console and hit
Enter to have R ‘learn’ these custom
functions.
If you want to use these custom functions in your own data, you will
need to change the variable names according to your own data (check out
the R code and you will see what we mean).
# calculate effect sizes ----
## skewness ----
calc.skewness <- function(x, output = "est") {
n <- length(x)
if (output == "est") { # skewness estimate
(sqrt(n * (n - 1)) / (n - 2)) *
(((1 / n) * sum((x - mean(x)) ^ 3)) /
(((1 / n) * sum((x - mean(x)) ^ 2)) ^ (3/2)))
} else if (output == "var") { # skewness sampling variance
(6 * n * (n - 1)) /
((n - 2) * (n + 1) * (n + 3))
}
}
## kurtosis ----
calc.kurtosis <- function(x, output = "est") {
n <- length(x)
if (output == "est") { # kurtosis estimate
((((n + 1) * n * (n - 1)) / ((n - 2) * (n - 3))) *
(sum((x - mean(x)) ^ 4) / (sum((x - mean(x)) ^ 2) ^ 2))) -
(3 * ((n - 1) ^ 2) / ((n - 2) * (n - 3)))
} else if (output == "var") { # kurtosis sampling variance
(24 * n * ((n - 1) ^ 2)) /
((n - 3) * (n - 2) * (n + 3) * (n + 5))
}
}
## Zr ----
r.to.zr <- # Zr estimate
function(r) {
0.5 * log((1 + r) / (1 - r))
}
zr.variance <- # Zr variance
function(n) {
1 / (n - 3)
}
## other effect sizes (lnRR and lnVR) ----
calc.effect <- function(data = raw_data,
m) { # calculates other already established effect size statistics
escalc(measure = m,
m1i = data$mean_male,
m2i = data$mean_female,
sd1i = data$sd_male,
sd2i = data$sd_female,
n1i = data$n_male,
n2i = data$n_female,
var.names = c(paste0(m,
"_est"),
paste0(m,
"_var")))
}
# processing functions ----
process.ind_effects <- function(chosen_trait = "fat_mass",
measure = "KU_delta") {
ind_effects <-
df_meta_analysed %>%
filter(trait_name == chosen_trait,
phenotyping_center %in% c("CCP-IMG",
"HMGU",
"JAX",
"MRC H",
"TCP")) %>%
mutate(type = "individual") %>%
select(phenotyping_center,
strain_fig,
n = n_total,
est = paste0(measure, "_", "est"),
var = paste0(measure, "_", "var"),
lower = paste0(measure, "_", "lower"),
upper = paste0(measure, "_", "upper"))
model <- rma.mv(data = ind_effects,
yi = est,
V = var,
test = "t",
random = list(~ 1|phenotyping_center,
~ 1|strain_fig))
df_model <- data.frame(trait_name = chosen_trait,
est = model$beta[1],
var = model$se ^ 2,
lower = model$ci.lb,
upper = model$ci.ub,
phenotyping_center = "Mean",
strain_fig = "ES")
ind_effects %>%
bind_rows(df_model) %>%
mutate(est_type = measure,
centre_and_strain = factor(paste0(phenotyping_center,
"\n",
strain_fig))) %>%
mutate(centre_and_strain = factor(centre_and_strain,
levels = c("Mean\nES",
rev(levels(centre_and_strain)[-6]))))
}
process.cor_effects <- function(chosen_trait_1 = "fat_mass",
chosen_trait_2 = "heart_weight") {
df_effects_cor <-
df_raw %>%
filter(trait_name %in% c(chosen_trait_1,
chosen_trait_2),
phenotyping_center %in% c("CCP-IMG",
"HMGU",
"JAX",
"MRC H",
"TCP")) %>%
pivot_wider(id_cols = c(specimen_id,
strain_fig,
phenotyping_center,
sex),
names_from = trait_name) %>%
clean_names() %>%
drop_na() %>%
group_by(strain_fig,
phenotyping_center,
sex) %>%
group_modify(~ correlate(.x)) %>%
drop_na(all_of(chosen_trait_2)) %>%
ungroup() %>%
left_join(df_raw %>%
filter(trait_name %in% c(chosen_trait_1,
chosen_trait_2),
phenotyping_center %in% c("CCP-IMG",
"HMGU",
"JAX",
"MRC H",
"TCP")) %>%
pivot_wider(id_cols = c(specimen_id,
strain_fig,
phenotyping_center,
sex),
names_from = trait_name) %>%
clean_names() %>%
drop_na() %>%
group_by(strain_fig,
phenotyping_center,
sex) %>%
summarise(n = n())) %>%
rename(r_est = chosen_trait_2) %>%
mutate(zr_est = r.to.zr(r_est),
zr_var = zr.variance(n)) %>%
select(- c(4:6)) %>%
pivot_wider(names_from = sex,
values_from = c(n,
zr_est,
zr_var)) %>%
mutate(delta_zr_est = zr_est_male - zr_est_female,
delta_zr_var = zr_var_male + zr_var_female,
delta_zr_upper = delta_zr_est +
qt(0.975, n_male + n_female - 2) *
sqrt(delta_zr_var),
delta_zr_lower = delta_zr_est -
qt(0.975, n_male + n_female - 2) *
sqrt(delta_zr_var))
mlma_zr <-
rma.mv(data = df_effects_cor,
yi = delta_zr_est,
V = delta_zr_var,
test = "t",
random = list(~ 1|phenotyping_center,
~ 1|strain_fig))
df_model <- data.frame(delta_zr_est = mlma_zr$beta[1],
delta_zr_lower = mlma_zr$ci.lb,
delta_zr_upper = mlma_zr$ci.ub,
phenotyping_center = "Mean",
strain_fig = "ES")
df_effects_cor %>%
bind_rows(df_model) %>%
mutate(centre_and_strain = factor(paste0(phenotyping_center,
"\n",
strain_fig))) %>%
mutate(centre_and_strain = factor(centre_and_strain,
levels = c("Mean\nES",
rev(levels(centre_and_strain)[-5]))))
}
# visualisation functions ----
caterpillar.custom <-
function(chosen_trait = "fat_mass",
measure = "KU_delta") {
plot <-
process.ind_effects(chosen_trait = chosen_trait,
measure = measure) %>%
ggplot(aes(y = centre_and_strain,
x = est,
xmax = upper,
xmin = lower,
shape = strain_fig,
col = phenotyping_center)) +
geom_pointrange() +
geom_vline(xintercept = 0,
linetype = "dotted") +
theme_classic() +
theme(legend.position = "none",
axis.text.y = element_blank(),
axis.title.y = element_blank(),
plot.tag.position = c(0.15, 0.98))
if (measure == "ROM") {
plot +
labs(x = "lnRR") +
scale_x_continuous(limits = c(-0.51, 0.51),
breaks = c(-0.5, 0, 0.5)) +
theme(axis.title.x = ggtext::element_markdown(face = "italic"))
} else if (measure == "VR") {
plot +
labs(x = "lnVR") +
scale_x_continuous(limits = c(-1, 1),
breaks = c(-1, 0, 1)) +
theme(axis.title.x = ggtext::element_markdown(face = "italic"))
} else if (measure == "SK_delta") {
plot +
labs(x = "Δ*sk*") +
scale_x_continuous(limits = c(-2.1, 2.1),
breaks = c(-2, 0, 2)) +
theme(axis.title.x = ggtext::element_markdown())
} else if (measure == "KU_delta") {
plot +
labs(x = "Δ*ku*") +
scale_x_continuous(limits = c(-15, 15),
breaks = c(-15, 0, 15)) +
theme(axis.title.x = ggtext::element_markdown())
}
}
ridgeline.custom <- function(chosen_trait = "fat_mass") {
df_raw %>%
filter(trait_name == chosen_trait,
phenotyping_center %in% c("CCP-IMG",
"HMGU",
"JAX",
"MRC H",
"TCP")) %>%
add_row(phenotyping_center = "Mean",
strain_fig = "ES") %>%
mutate(centre_and_strain = factor(paste0(phenotyping_center,
"\n",
strain_fig))) %>%
mutate(centre_and_strain = factor(centre_and_strain,
levels = c("Mean\nES",
rev(levels(centre_and_strain)[-5]))),
value_s = scale(value)) %>%
ggplot(aes(x = value_s,
y = centre_and_strain,
fill = sex,
linetype = sex)) +
stat_slab(scale = 0.7,
alpha = 0.4,
linewidth = 0.6,
col = "black") +
scale_fill_manual(values = c("white",
"black")) +
scale_linetype_manual(values = c("solid",
"dashed")) +
labs(x = paste0(str_to_sentence(str_replace_all(chosen_trait,
"_",
" ")),
"\n(scaled)"),
y = "Phenotyping centre and mice strain") +
theme_classic() +
theme(legend.position = "none",
axis.title.x = element_text(size = 12,
margin = margin(t = 0.2,
unit = "cm")),
axis.title.y = element_text(size = 12,
margin = margin(r = 0.2,
unit = "cm")),
axis.text.x = element_text(size = 10),
axis.text.y = element_text(size = 10),
plot.tag.position = c(0.53, 0.98))
}
cor.caterpillar.custom <-
function(chosen_trait_1 = "fat_mass",
chosen_trait_2 = "heart_weight") {
process.cor_effects(chosen_trait_1 = chosen_trait_1,
chosen_trait_2 = chosen_trait_2) %>%
ggplot(aes(y = centre_and_strain,
x = delta_zr_est,
xmax = delta_zr_upper,
xmin = delta_zr_lower,
shape = strain_fig,
col = phenotyping_center)) +
geom_pointrange() +
geom_vline(xintercept = 0,
linetype = "dotted") +
labs(y = "Phenotyping centre and mice strain",
x = "Δ*Zr*",
shape = "Strain") +
scale_x_continuous(limits = c(-1, 1),
breaks = c(-1, 0, 1)) +
theme_classic() +
theme(legend.position = "none",
axis.title.x = ggtext::element_markdown(size = 12,
margin = margin(t = 0.2,
unit = "cm")),
axis.title.y = element_text(size = 12,
margin = margin(r = - 0.1,
unit = "cm")),
axis.text.x = element_text(size = 10),
axis.text.y = element_text(size = 10),
plot.tag.position = c(0.3, 0.99))
}
cor.plot.custom <-
function(chosen_trait_1 = "fat_mass",
chosen_trait_2 = "heart_weight",
chosen_lims = c(-3, 5)) {
df_cor <-
df_raw %>%
filter(trait_name %in% c(chosen_trait_1,
chosen_trait_2),
phenotyping_center %in% c("CCP-IMG",
"HMGU",
"JAX",
"MRC H",
"TCP")) %>%
pivot_wider(id_cols = c(specimen_id,
strain_fig,
phenotyping_center,
sex),
names_from = trait_name) %>%
clean_names() %>%
drop_na() %>%
mutate(centre_and_strain = factor(paste0(phenotyping_center,
strain_fig))) %>%
mutate(centre_and_strain = factor(centre_and_strain,
levels = rev(levels(centre_and_strain))),
trait_1_s = scale(get(chosen_trait_1))[,1],
trait_2_s = scale(get(chosen_trait_2))[,1])
plot_list <- list()
for (i in 1:length(levels(df_cor$centre_and_strain))) {
level_i <- sort(levels(df_cor$centre_and_strain))[i]
plot <-
df_cor %>%
filter(centre_and_strain == level_i) %>%
ggplot(aes(x = trait_1_s,
y = trait_2_s,
shape = sex,
linetype = sex)) +
geom_point(
alpha = 0.008,
) +
geom_abline(intercept = 0,
slope = 1,
linewidth = 0.5,
linetype = "dotted") +
geom_smooth(method = "lm",
se = F,
col = "black") +
scale_shape_manual(values = c(3, 4)) +
scale_linetype_manual(values = c("solid",
"dashed")) +
scale_x_continuous(limits = chosen_lims) +
scale_y_continuous(limits = chosen_lims) +
labs(x = paste0(str_to_sentence(str_replace_all(chosen_trait_1,
"_",
" ")),
"\n(scaled)"),
y = paste0(str_to_sentence(str_replace_all(chosen_trait_2,
"_",
" ")),
" (scaled)")) +
theme_classic() +
theme(legend.position = "none",
plot.tag.position = c(0.05, 0.91),
axis.title.x = element_text(size = 12,
margin = margin(t = 0.2,
unit = "cm")),
axis.title.y = element_text(size = 12,
margin = margin(r = 0.2,
unit = "cm")),
axis.text.x = element_text(size = 10),
axis.text.y = element_text(size = 10))
if (i != 6) {
plot <-
plot +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.line.x = element_blank(),
axis.ticks.x = element_blank())
}
plot_list[[i]] <- plot
}
return(plot_list)
}Following Pick et al. (2022).
\[ sk = \frac{\frac{1}{n} \sum_{i = 1}^{n}(x_{i} - \bar{x}) ^ 3}{[\frac{1}{n} \sum_{i = 1}^{n}(x - \bar{x}) ^ 2] ^ \frac{3}{2}} \frac{\sqrt{n (n - 1)}}{n - 2} \] \[ s^2_{sk} = \frac{6n(n - 1)}{(n - 2)(n + 1)(n + 3)} \]
\[ \Delta sk = sk_{1} - sk_{2} \]
\[ s^2_{\Delta sk} = s^2_{sk_1} + s^2_{sk_2} - 2 \rho_{sk} s_{sk_1} s_{sk_2} \]
\[ ku = \frac{n (n + 1) (n - 1)}{(n - 2)(n - 3)} \frac{\sum_{i = 1}^{n}(x_{i} - \bar{x}) ^ 4} {[\sum_{i = 1}^{n}(x_{i} - \bar{x}) ^ 2]^ 2} - \frac{3(n - 1) ^ 2}{(n - 2)(n - 3)} \] \[ s^2_{ku} = \frac{24 n (n - 1) ^ 2}{(n - 3)(n - 2)(n + 3)(n + 5)} \]
\[ \Delta ku = ku_{1} - ku_{2} \]
\[ s^2_{\Delta ku} = s^2_{ku_1} + s^2_{ku_2} - 2 \rho_{ku} s_{ku_1} s_{ku_2} \]
\[ Zr = \frac{ln(\frac{1 + r}{1 - r})}{2} \]
\[ s^2_{Zr} = \frac{1}{n - 3} \] \[ \Delta Zr = Zr_{1} - Zr_{2} \]
\[ s^2_{\Delta Zr} = s^2_{Zr_1} + s^2_{Zr_2} -2 \rho_{Zr} s_{Zr_1} s_{Zr_2} \]
We use data from the International Mouse Phenotyping Consortium (IMPC, version 18.0; Dickinson et al., 2016; http://www.mousephenotype.org/).
# raw data ----
df_raw <-
read_csv("mice_data_sample.csv") %>%
# small adjustments to make plots more readable:
mutate(phenotyping_center =
ifelse(phenotyping_center == "MRC Harwell",
"MRC H",
phenotyping_center),
strain_fig = case_when(strain_accession_id == "MGI:2159965" ~
"N",
strain_accession_id == "MGI:2683688" ~
"NCrl",
strain_accession_id == "MGI:2164831" ~
"NTac",
strain_accession_id == "MGI:3056279" ~
"NJ",
strain_accession_id == "MGI:2160139" ~
"NJcl"))
df_meta_analysed <-
df_raw %>%
group_by(sex,
trait_name,
phenotyping_center,
strain_fig) %>%
summarize(mean = mean(value,
na.rm = T),
sd = sd(value,
na.rm = T),
n = n(),
SK_est = calc.skewness(value),
SK_var = calc.skewness(value, output = "var"),
KU_est = calc.kurtosis(value),
KU_var = calc.kurtosis(value, output = "var")) %>%
pivot_wider(id_cols = c(trait_name,
phenotyping_center,
strain_fig),
names_from = sex,
values_from = c(mean:KU_var)) %>%
mutate(SK_delta_est = SK_est_male - SK_est_female,
SK_delta_var = SK_var_male + SK_var_female,
KU_delta_est = KU_est_male - KU_est_female,
KU_delta_var = KU_var_male + KU_var_female) %>%
bind_cols(calc.effect(., m = "ROM")) %>% # lnRR
bind_cols(calc.effect(., m = "CVR")) %>% # lnCVR
bind_cols(calc.effect(., m = "VR")) %>% # lnVR
filter(!is.na(CVR_est)) %>%
mutate(n_total = n_female + n_male,
prop_females = n_female / (n_female + n_male)) %>%
select(trait_name,
phenotyping_center,
strain_fig,
n_total,
prop_females,
ROM_est,
ROM_var,
CVR_est,
CVR_var,
VR_est,
VR_var,
SK_delta_est,
SK_delta_var,
KU_delta_est,
KU_delta_var) %>%
mutate(ROM_upper = ROM_est + qt(0.975,
n_total - 1) * sqrt(ROM_var),
ROM_lower = ROM_est - qt(0.975,
n_total - 1) * sqrt(ROM_var),
CVR_upper = CVR_est + qt(0.975,
n_total - 1) * sqrt(CVR_var),
CVR_lower = CVR_est - qt(0.975,
n_total - 1) * sqrt(CVR_var),
VR_upper = VR_est + qt(0.975,
n_total - 1) * sqrt(VR_var),
VR_lower = VR_est - qt(0.975,
n_total - 1) * sqrt(VR_var),
SK_delta_upper = SK_delta_est + qt(0.975,
n_total - 1) * sqrt(SK_delta_var),
SK_delta_lower = SK_delta_est - qt(0.975,
n_total - 1) * sqrt(SK_delta_var),
KU_delta_upper = KU_delta_est + qt(0.975,
n_total - 1) * sqrt(KU_delta_var),
KU_delta_lower = KU_delta_est - qt(0.975,
n_total - 1) * sqrt(KU_delta_var))We then use the data from multiple phenotyping centres and mice strains to calculate average effect sizes (\(\Delta sk\), \(\Delta ku\), and \(\Delta Zr\)).
map2_dfr(.x = rep(c("fat_mass",
"heart_weight",
"glucose",
"total_cholesterol"),
each = 4),
.y = rep(c("ROM",
"VR",
"SK_delta",
"KU_delta"),
4),
.f = process.ind_effects) %>%
mutate(est_type = case_when(est_type == "ROM" ~ "lnRR",
est_type == "VR" ~ "lnVR",
est_type == "SK_delta" ~ "delta_sk",
est_type == "KU_delta" ~ "delta_ku")) %>%
datatable(.,
extensions = "Buttons",
rownames = FALSE)map2_dfr(.x = c("fat_mass",
"glucose"),
.y = c("heart_weight",
"total_cholesterol"),
.f = process.cor_effects) %>%
mutate(relationship = rep(c("fat mass and heart weight",
"glucose and total cholesterol"),
each = 7)) %>%
relocate(relationship) %>%
datatable(.,
extensions = "Buttons",
rownames = FALSE)
## Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
## ℹ Please use `all_of()` or `any_of()` instead.
## # Was:
## data %>% select(chosen_trait_2)
##
## # Now:
## data %>% select(all_of(chosen_trait_2))
##
## See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## figure_2 ----
list_figure_2 <- list()
list_figure_2[[1]] <- ridgeline.custom("fat_mass")
list_figure_2[2:5] <- map2(.x = rep("fat_mass", 4),
.y = c("ROM",
"VR",
"SK_delta",
"KU_delta"),
.f = caterpillar.custom)
list_figure_2[[6]] <- ridgeline.custom("heart_weight")
list_figure_2[7:10] <- map2(.x = rep("heart_weight", 4),
.y = c("ROM",
"VR",
"SK_delta",
"KU_delta"),
.f = caterpillar.custom)
(figure_2 <-
list_figure_2 %>%
wrap_plots(ncol = 5) +
plot_annotation(tag_levels = "A"))
## Warning: Removed 1 row containing missing values or values outside the scale range
## (`stat_slabinterval()`).
## Removed 1 row containing missing values or values outside the scale range
## (`stat_slabinterval()`).
## figure_3 ----
list_figure_3 <- list()
list_figure_3[1:6] <- cor.plot.custom(chosen_trait_1 = "fat_mass",
chosen_trait_2 = "heart_weight")
list_figure_3[[7]] <- cor.caterpillar.custom(chosen_trait_1 = "fat_mass",
chosen_trait_2 = "heart_weight")
list_figure_3[8:13] <- cor.plot.custom(chosen_trait_1 = "glucose",
chosen_trait_2 = "total_cholesterol")
list_figure_3[[14]] <- cor.caterpillar.custom(chosen_trait_1 = "glucose",
chosen_trait_2 = "total_cholesterol")
(figure_3 <-
list_figure_3 %>%
wrap_plots() +
plot_layout(design = layout_2,
heights = c(rep(1, 6), 0.6),
widths = c(rep(0.23, 2), 0.02, rep(0.23, 2)),
axes = "collect",
guides = "collect") +
plot_annotation(tag_levels = list(c("A",
rep("",
5),
"B",
"C",
rep("",
5),
"D"))))
## Warning: Removed 4 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 4 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 3 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 3 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 27 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 27 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 3 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 3 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 25 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 25 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 3 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 3 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_point()`).
## figure_4 ----
list_figure_4 <- list()
list_figure_4[[1]] <- ridgeline.custom("glucose")
list_figure_4[2:5] <- map2(.x = rep("glucose", 4),
.y = c("ROM",
"VR",
"SK_delta",
"KU_delta"),
.f = caterpillar.custom)
list_figure_4[[6]] <- ridgeline.custom("total_cholesterol")
list_figure_4[7:10] <- map2(.x = rep("total_cholesterol", 4),
.y = c("ROM",
"VR",
"SK_delta",
"KU_delta"),
.f = caterpillar.custom)
(figure_4 <-
list_figure_4 %>%
wrap_plots(ncol = 5) +
plot_annotation(tag_levels = "A"))
## Warning: Removed 1 row containing missing values or values outside the scale range
## (`stat_slabinterval()`).
## Warning: Removed 1 row containing missing values or values outside the scale range
## (`stat_slabinterval()`).
sessionInfo() %>%
pander()R version 4.5.1 (2025-06-13)
Platform: aarch64-apple-darwin20
locale: en_US.UTF-8||en_US.UTF-8||en_US.UTF-8||C||en_US.UTF-8||en_US.UTF-8
attached base packages: stats, graphics, grDevices, utils, datasets, methods and base
other attached packages: lubridate(v.1.9.4), forcats(v.1.0.0), stringr(v.1.5.1), dplyr(v.1.1.4), purrr(v.1.0.4), readr(v.2.1.5), tidyr(v.1.3.1), tibble(v.3.3.0), ggplot2(v.3.5.2), tidyverse(v.2.0.0), patchwork(v.1.3.1), pander(v.0.6.6), metafor(v.4.8-0), numDeriv(v.2016.8-1.1), metadat(v.1.4-0), Matrix(v.1.7-3), janitor(v.2.2.1), here(v.1.0.1), ggtext(v.0.1.2), ggdist(v.3.3.3), DT(v.0.33), corrr(v.0.4.4) and pacman(v.0.5.1)
loaded via a namespace (and not attached): gtable(v.0.3.6), xfun(v.0.52), bslib(v.0.9.0), htmlwidgets(v.1.6.4), lattice(v.0.22-7), mathjaxr(v.1.8-0), tzdb(v.0.5.0), vctrs(v.0.6.5), tools(v.4.5.1), crosstalk(v.1.2.1), generics(v.0.1.4), parallel(v.4.5.1), pkgconfig(v.2.0.3), RColorBrewer(v.1.1-3), distributional(v.0.5.0), lifecycle(v.1.0.4), compiler(v.4.5.1), farver(v.2.1.2), snakecase(v.0.11.1), litedown(v.0.7), htmltools(v.0.5.8.1), sass(v.0.4.10), yaml(v.2.3.10), pillar(v.1.10.2), crayon(v.1.5.3), jquerylib(v.0.1.4), cachem(v.1.1.0), nlme(v.3.1-168), commonmark(v.1.9.5), tidyselect(v.1.2.1), digest(v.0.6.37), stringi(v.1.8.7), splines(v.4.5.1), labeling(v.0.4.3), rprojroot(v.2.0.4), fastmap(v.1.2.0), grid(v.4.5.1), cli(v.3.6.5), magrittr(v.2.0.3), withr(v.3.0.2), scales(v.1.4.0), bit64(v.4.6.0-1), timechange(v.0.3.0), rmarkdown(v.2.29), bit(v.4.6.0), hms(v.1.1.3), evaluate(v.1.0.4), knitr(v.1.50), mgcv(v.1.9-3), markdown(v.2.0), rlang(v.1.1.6), gridtext(v.0.1.5), Rcpp(v.1.0.14), glue(v.1.8.0), xml2(v.1.3.8), vroom(v.1.6.5), jsonlite(v.2.0.0) and R6(v.2.6.1)